shipinfenxi

经典的mallat算法程序。用mallat算法实现低通，高通滤波器。(Mallat algorithm classical procedures. Mallat algorithm using low-pass, high pass filter.)

Find peaks in all waveforms. used in matlab. The peaks can be maximum and minimum.

用实现的维纳滤波器，三个参数分别是信号源，以及作为样本的信号时间（单位秒）(Wiener filter is implemented using matlab. three parameters are the source, fs, as well as samples of the signal time (in seconds))

PSO algorithm matlab code

通过matlab程序实现的，基于块匹配的运动估计，预测图像(By matlab program, the block-matching based motion estimation, prediction image)

matlab Mutual information 源代码(matlab Mutual information source code)

对于非线性时变工业过程对象，首先建立模型集来覆盖被控对象动态特性，分别建立相应的内模控制器，在系统变工况运行时，进行多模型加权自适应控制，与传统单内模控制相比，控制效果明显提高。(For the nonlinear and time-varying industrial process object, first ，we build model set to cover the dynamic characteristics of the object, then,set up a corresponding internal model controller respectively.When the system operation mode is running, multiple model weighted adaptive control has been used,and compared with the traditional single internal model control,it has improved obviously. )

小波去噪matlab仿真算法，用于ECG心电信号去噪(wavelet filter Matlab)

语音信号处理中的的线性特征，能量熵的计算，这是第二能量熵的matlab代码，希望大家能够借鉴一下(Speech signal processing linear features, energy entropy calculation, which is the second energy entropy matlab code, I hope we can learn about)

：该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift 跟踪算法，提出了基于多 特征的Mean Shift 人脸跟踪算法以解决Mean shift 跟踪算法的鲁棒性问题。通过对LTP 纹理特征的分析、研究， 提出了一个LTP 关键纹理模型，既增强了目标的关键纹理信息，又简化了LTP 纹理模型。在此基础上，提出一 种基于LTP 关键纹理特征和肤色特征的Mean Shift 人脸跟踪算法，有效地解决了Mean Shift 算法的鲁棒性问题。 为进一步提高对快速运动目标的跟踪速度和跟踪性能，该文引入了卡尔曼滤波器对目标进行预测。实验结果表明， 该文的算法在目标定位的准确性和跟踪性能上比Mean Shift 算法均有明显的提高。(: In this paper, the texture characteristics of the local ternary patterns Local Ternary Patterns (LTP) Mean Shift tracking algorithm proposed Mean Shift face tracking algorithm based on multiple features in order to solve the Mean shift tracking algorithm robustness. Texture characteristics of LTP analysis, research, a key LTP texture model, not only enhanced the key goal of texture information, but also simplifies the the LTP texture model. On this basis, based on the LTP key texture features and color characteristics Mean Shift face tracking algorithm, effectively solved the robustness of the Mean Shift algorithm. To further enhance the fast-moving target tracking speed and tracking performance, this paper introduces the Kalman filter to predict the target. The experimental results show that the algorithm of the text in the target positioning accuracy and tracking performance than the Mean Shift algorithm to significantly improve.)